A novel locally adaptive method for modeling the spatiotemporal dynamics of global electric power consumption based on DMSP-OLS nighttime stable light data
Introduction
Along with the tremendous development of the global economy, energy demand has continuously increased over the last century [1], [2]. As an indispensable component of energy, electric power plays a vital role in numerous aspects of modern society, such as improving residential living standards [3], supporting industrial production [4], and promoting commercial transactions [5]. According to the World Bank [6], global electric power consumption (EPC) in 2014 was more than four times higher than that in 1971. In addition to the convenience brought by the massive increase of EPC, the world has also been burdened with accelerated global warming and air pollution due to the accompanying emission of greenhouse gases and other pollutants [7], [8]. Therefore, accurate delineation of the spatiotemporal dynamics of global EPC is a critical prerequisite for investigating both the impacts of EPC and its interaction with the economy and the environment [9], [10].
A wealth of research has investigated the spatiotemporal dynamics of EPC based on the EPC statistics published by related official organizations. For instance, AI-Garni et al. [11] adopted a regression model to forecast EPC in Eastern Saudi Arabia using weather data, global solar radiation, and population as variables. Egelioglu et al. [12] predicted annual EPC by multiple regression analyses of the historical economic databases and EPC statistics for Northern Cyprus. Shiu and Lam [13] examined the causal relationship between EPC and GDP in China by the error-correction model. Huang et al. [14] investigated the electric power supply and demand in China using the Grey-Markov forecasting model. Chujai et al. [15] forecasted the EPC at a household scale with different autoregressive models based on time-series EPC statistics. Cabral et al. [16] developed a spatiotemporal method that considers spatial correlations to predict the EPC in Brazil. These previous studies have been devoted to providing suggestions for governments or organizations. However, for the EPC statistics, the collection process is labor-intensive and time-consuming. Moreover, the EPC statistics are unable to reflect the internal spatial details within the administrative unit, which limits our understanding of the spatiotemporal dynamics of EPC at smaller scales [17], [18]. Compared with the statistics for an entire administrative unit, gridding is a more realistic representation for the investigation of EPC at finer scales. Therefore, efficient methods to produce a spatially gridded representation of worldwide EPC are urgently needed, and it is worth attempting to adopt appropriate spatial gridded data as a proxy for modeling global EPC.
Satellite remotely sensed imagery has been proved to be a reliable way to support large-scale investigations in numerous fields, such as global solar radiation [19], land surface temperature [20], land use and land cover [21], and CO2 emission [22]. The nighttime light (NTL) remote sensing imagery, such as that obtained by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) [23], has the potential for EPC estimation over large areas, because NTL can directly reflect the EPC caused by anthropogenic socio-economic activities at night [24], [25], [26], [27]. Elvidge et al. [28] verified the high log-log relation between the lit areas in DMSP-OLS data and EPC for 200 countries during 1994–1995. Similarly, Lo [29] modeled the logarithmic relationship between DMSP-OLS NTL and EPC for 35 Chinese cities for the year 1997. Amaral et al. [30] found that DMSP-OLS NTL was linearly correlated with the statistical EPC for 1999 in Brazilian Amazonia. Chand et al. [31] analyzed the linear relationship between the increase of EPC and the increase of NTL in the major cities and states of India during 1993–2002. Townsend and Bruce [32] reported a strong second-order polynomial relationship between DMSP-OLS NTL and EPC at the state level in Australia for 1997–2002. Letu et al. [33] estimated EPC in Japan and other Asian countries from saturated-corrected DMSP-OLS data, and found a strong linear correlation [34] between EPC and DMSP-OLS data in Japan. He et al. [35] respectively modeled double-log relationships for different economic regions of the Chinese Mainland from 1995 to 2008 at the county level. Ma et al. [36] attempted three models (linear, power-law, and exponential function) to quantify the relationships between EPC statistics and DMSP-OLS NTL for more than 200 cities in China during 1994–2009, and suggested that the best quantitative model type varies with the different socioeconomic patterns. Xie and Weng [37] explored the country-level relationship between EPC statistics and DMSP-OLS NTL by the logarithmic function. Jing et al. [38] adopted the linear model to correlate EPC with DMSP-OLS NTL data at the provincial level in China. By summarizing the existing literature, it can be found that different types of models have been utilized across different regions when using NTL to estimate EPC, due to the disparity of the social, economic, and urban development status among the different regions.
With respect to the estimation of spatially gridded EPC, Zhao et al. [39] estimated the provincial-level EPC based on the DMSP-OLS and population data in China, and generated pixel-level EPC through disaggregation. Cao et al. [40] proposed a statistics-to-grid scaling down method for mapping gridded EPC in China based on the integration of DMSP-OLS data, population and gross domestic product (GDP). He et al. [41] modeled annual pixel-based EPC in Chinese Mainland with DMSP-OLS and normalized difference vegetation index (NDVI). Xie and Weng [42] estimated gridded EPC of China using DMSP-OLS data, population and enhanced vegetation index (EVI) considering the difference between urban cores and suburban areas. Pan and Li [43] generated 1-km EPC map in China with different vegetation indices and DMSP-OLS data. Most of the existing studies have focused on modeling at the national, regional, or city level, however, research at the global scale is scarce, due to its complexity. An exception and a notable example is the study of Shi et al. [44], where the original NTL images were first corrected worldwide using a uniform framework, and the world was then partitioned into 48 regions according to the geographic locations and socioeconomic levels. Finally, a linear model between the EPC statistics and corrected NTL data for each region was individually built to explore the gridded EPC. Nonetheless, this method does not fully consider the influence of the uniqueness of local socioeconomic development on the following three aspects in the EPC estimation.
- (1)
The saturation issue of NTL data: The relatively low radiometric resolution (6 bits) of the OLS sensor results in saturation in the NTL data [45], especially in the centers of large cities. All digital number (DN) values of these saturated pixels are 63, and hence, the disparity within the urban centers cannot be distinguished. In Shi et al. [44], a modified invariant region (MIR) method was globally adopted to reduce the saturated pixels. Nevertheless, saturated pixels are not ubiquitous worldwide, especially in underdeveloped areas. Saturation correction can result in distortion of these unsaturated pixels in suburban and rural areas [46], and reduce the contribution of saturated pixels to the EPC estimation [42]. Therefore, it is not appropriate to globally utilize a unified framework for saturation correction.
- (2)
The incomparability and discontinuity effect of NTL data: The original NTL images cannot be directly compared with each other, due to the lack of onboard calibration for the OLS sensor. Specifically, the DN values in the images obtained from the same satellite fluctuate abnormally in different years, and discrepancies occur in the images collected by different satellites for the same year [47]. Shi et al. [44] performed inter-annul correction in a forward direction for the whole world to eliminate the abnormal fluctuation (i.e., discontinuity effect) [43], [44], [48]. However, in addition to the forward direction, other approaches (e.g., backward, average) can also be considered for the correction, resulting in different corrected NTL data [49], [50]. With inappropriately corrected NTL data, the reliability and accuracy of EPC estimation can also be affected. Therefore, it is not reasonable to apply the same approach (e.g., forward) for all the regions with diverse socioeconomic dynamics throughout the world.
- (3)
The estimation model between the EPC statistics and the corrected NTL data: Shi et al. [44] employed linear models for all the regions in the world. However, as indicated by the previous studies [35─38], the appropriate type of regression model can vary across areas, owing to the local socioeconomic diversity. Therefore, it is inappropriate to limit the model type to the linear one at the global scale.
To address the aforementioned research questions, we propose a novel locally adaptive method for modeling global EPC. Since the available EPC statistics across the globe are at the national level, the local scale in this study is set as the country/district level. Specifically, for each country/district, two options (with or without correction) are first designed for saturation correction, and three optional directions (forward, backward, or average) are secondly considered for the inter-annual correction. Four alternative models (linear, logarithmic, exponential, or second-order polynomial functions) are then set up to reflect the possible relationships between the EPC and NTL data. Finally, the processing chain composed of the optimal options in the three aspects is adaptively selected for each country/district, to accommodate the local socioeconomic status.
The rest of this paper is organized as follows. Section 2 focuses on the data sources used in this study. Section 3 introduces the proposed locally adaptive selection method for global EPC mapping. The results and discussion are respectively presented in 4 Results, 5 Discussion. Finally, Section 6 sets out our main conclusions.
Section snippets
Data sources
The Version 4 global nighttime stable light (NSL) data of DMSP-OLS for 1992–2013 were obtained from the National Oceanic and Atmospheric Administration-National Geophysical Data Center (NOAA/NGDC) website (http://www.ngdc.noaa.gov/eog/dmsp.html). The NSL images were acquired by six satellites: F10 (1992–1994), F12 (1994–1999), F14 (1997–2003), F15 (2000–2007), F16 (2004–2009), and F18 (2010–2013), covering an area from −180 to 180 degrees in longitude and −65 to 75 degrees in latitude. The 34
Methodology
The proposed locally adaptive method for modeling global EPC consists of four main procedures: (1) decomposition of the global NSL images into national NSL data based on the national boundaries; (2) sequential connection of all possible options in the NSL data correction (including the saturation correction and inter-annual correction) and EPC estimation to form all the candidate processing chains; (3) locally adaptive selection of the optimal processing chains to construct the global EPC; (4)
The saturation correction
To clearly show the effects of the saturation correction on the NSL data for areas with different socioeconomic levels, we visually compared the original NSL with saturation-corrected (VANUI) images for six cities in 2013, using the 30-m resolution Landsat 8 OLI images and the VIIRS/DNB data as the reference to show the urban regions (Fig. 2). Afghanistan, China, and the United States were selected as representative countries, considering their different development levels. Within each country,
Comparison with existing global products
Shi et al. [44] modeled 1-km resolution global EPC maps from 1992 to 2013 by dividing the world into a series of regions, with a linear regression model for each region (hereafter referred to as Shi’s product). Here we compare our estimated global EPC maps with Shi’s product at global, continental, and national levels, respectively, based on the country-level statistics.
Conclusion
The DMSP-OLS nighttime stable light (NSL) images have the ability to model gridded electricity power consumption (EPC) across the globe. However, we need to properly deal with the saturation problem, as well as the incomparability and discontinuity issues existing in the original NSL data, to make the data a reasonable approximation of EPC. The regression model can then be built to quantify the relationship between the EPC statistics and the corrected NSL for the gridded EPC estimation.
Acknowledgements
The research was supported by the National Natural Science Foundation of China under Grant 41771360, the National Program for Support of Top-notch Young Professionals, the Hubei Provincial Natural Science Foundation of China under Grant 2017CFA029, and the National Key R&D Program of China under Grant 2016YFB0501403.
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